Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks

arXiv:2310.06743v2 Announce Type: replace
Abstract: Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features. These embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, little attention has been paid to the exact design …

arxiv cs.ai cs.lg encoding location networks representation type